EEG in game user analysis: A framework for expertise classification during gameplay
| Autor: | Muhammad Usman Ashraf, Khalid Alsubhi, Sanay Muhammad Umar Saeed, Aamir Arsalan, Tehmina Hafeez, Syed Muhammad Anwar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Physiology Computer science Emotions Social Sciences Wearable computer 02 engineering and technology Electroencephalography computer.software_genre Machine Learning Cognition Medicine and Health Sciences 0202 electrical engineering electronic engineering information engineering Psychology Attention Video game design 050107 human factors Clinical Neurophysiology Brain Mapping Multidisciplinary Multimedia medicine.diagnostic_test Applied Mathematics Simulation and Modeling 05 social sciences Built Structures Electrophysiology Bioassays and Physiological Analysis Brain Electrophysiology Physical Sciences Engineering and Technology Medicine Female 020201 artificial intelligence & image processing Games Algorithms Research Article Adult Computer and Information Sciences Competitive Behavior Structural Engineering Imaging Techniques Headset Science Neurophysiology Neuroimaging User analysis Research and Analysis Methods Machine learning Machine Learning Algorithms Young Adult Artificial Intelligence Support Vector Machines Classifier (linguistics) medicine Humans 0501 psychology and cognitive sciences Video game Behavior business.industry Electrophysiological Techniques Cognitive Psychology ComputingMilieux_PERSONALCOMPUTING Biology and Life Sciences Achievement Self Concept Support vector machine ComputingMethodologies_PATTERNRECOGNITION Video Games Recreation Cognitive Science Artificial intelligence Clinical Medicine business Classifier (UML) computer Mathematics Neuroscience |
| Zdroj: | PLoS ONE, Vol 16, Iss 6, p e0246913 (2021) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help provide an optimal gaming experience level by improving video game design. To this end, we present a framework for classifying the game player’s expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert/novice players’ brain activity is different, which can be classified using the frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach identifies two significant EEG channels, i.e., AF3 and P7, from the Emotiv EPOC headset’s fourteen channels. The features extracted from these EEG channels contribute the most to the video game player’s expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying the game player’s expertise level with up to 98.04% classification accuracy.Author summaryTehmina Hafeez ROLES Investigation, Writing – original draft * E-mail: tehminamalik.52@gmail.com AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Sanay Muhammad Umar Saeed (Corresponding author) ROLES Conceptualization, Writing – review editing * E-mail: sanay.muhammad@uettaxila.edu.pk AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Aamir Arsalan ROLES Methodology, Writing – review editing * E-mail: aamir.arsalan@uettaxila.edu.pk AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Syed Muhammad Anwar ROLES Validation, Writing – review editing * E-mail: s.anwar@uettaxila.edu.pk AFFILIATION Department of Software Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Muhammad Usman Ashraf (Corresponding author) ROLES Validation, Writing – review editing * E-mail: usman.ashraf@skt.umt.edu.pk AFFILIATION Department of Computer Science, University of management and Technology, Lahore (Sialkot), 51040, Pakistan.Khalid Alsubhi ROLES Conceptualization, Writing – review editing AFFILIATION Department of Computer Science, King Abdul Aziz University, Jeddah, Saudi Arabia. |
| Databáze: | OpenAIRE |
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